Senior Machine Learning Engineer

We’re Hiring : Senior ML Engineer (Gen AI, Machine Learning)

Mandatory Skills : Graph ML, Agentic AI

Location : Remote

Domain :

Generative AI | Machine Learning | Distributed Systems

Overview

Are you energized by the idea of innovating with

Generative AI ?

Do you want to build

next-gen AI-driven products with real-world global impact ?

This is a

greenfield opportunity

to architect and scale intelligent AI systems across

Generative AI, Machine Learning, Big Data, and Cloud Computing . You’ll contribute directly to the

vision, technical roadmap, and user experience —from concept to production.

Our

AI Core Group

is pioneering intelligent platforms powered by :

⚡ AI Agents

⚡ RAG systems & Knowledge Bases

⚡ Graph ML

⚡ Data Mining & Anomaly Detection

⚡ LLM Fine-tuning & Optimization

We are redefining how enterprises leverage AI by building

multi-agent autonomous systems that perceive, reason, and act in real time.

Key Responsibilities

Act as an AI thought leader, driving strategic innovation across products and platforms

Lead the full SDLC — design ➝ testing ➝ deployment ➝ operations

Participate in architecture reviews, design reviews, code reviews & technical strategy

Build high-performance, production-grade ML systems for real-time AI platforms

Extend and optimize ML / GenAI libraries, frameworks & internal toolkits

Collaborate with AI scientists & platform engineers to accelerate experimentation & deployments

Deploy and scale ML solutions across cloud-based distributed systems

Requirements

• Degree in Computer Science, Mathematics, or a related field

8+ years of experience

across the full SDLC (design → development → deployment → operations)

5+ years of building & deploying end-to-end ML solutions in production

RAG, AI Agents, LLM Fine-tuning and Gen AI

in production

AWS / Azure / GCP

& distributed systems

complex & ambiguous problems

Preferred Qualifications

⭐ MS / PhD in CS, ML or related field

⭐ Expertise in

Graph ML & Graph technologies

(GNNs, GraphRAG, etc.)

⭐ Experience with distributed

Big Data ecosystems :

Spark

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